Initial commit

README.md

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+---
+language:
+- ru
+tags:
+- sentiment
+- text-classification
+---
+
+# RuBERT for Sentiment Analysis of Medical Reviews
+
+This is a [DeepPavlov/rubert-base-cased-conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model trained on corpus of medical reviews.
+
+## Labels
+    0: NEUTRAL
+    1: POSITIVE
+    2: NEGATIVE
+
+## How to use
+```python
+
+import torch
+from transformers import AutoModelForSequenceClassification
+from transformers import BertTokenizerFast
+
+tokenizer = BertTokenizerFast.from_pretrained('blanchefort/rubert-base-cased-sentiment-med')
+model = AutoModelForSequenceClassification.from_pretrained('blanchefort/rubert-base-cased-sentiment-med', return_dict=True)
+
+@torch.no_grad()
+def predict(text):
+    inputs = tokenizer(text, max_length=512, padding=True, truncation=True, return_tensors='pt')
+    outputs = model(**inputs)
+    predicted = torch.nn.functional.softmax(outputs.logits, dim=1)
+    predicted = torch.argmax(predicted, dim=1).numpy()
+    return predicted
+```
+
+
+## Dataset used for model training
+
+**[Отзывы о медучреждениях](https://github.com/blanchefort/datasets/tree/master/medical_comments)**
+
+> Датасет содержит пользовательские отзывы о медицинских учреждениях. Датасет собран в мае 2019 года с сайта prodoctorov.ru

config.json

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+{
+  "_name_or_path": "blanchefort/rubert-base-cased-sentiment-med",
+  "architectures": [
+    "BertForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "NEUTRAL",
+    "1": "POSITIVE",
+    "2": "NEGATIVE"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "NEUTRAL": 0,
+    "POSITIVE": 1,
+    "NEGATIVE": 2
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "output_past": true,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "return_dict": true,
+  "type_vocab_size": 2,
+  "vocab_size": 119547
+}

pytorch_model.bin

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special_tokens_map.json

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tf_model.h5

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tokenizer_config.json

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